NASA-MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools are clearly distinct, targeting specific NASA datasets or resources like asteroids, Earth imagery, Mars rover photos, and space weather events. However, some overlap exists: get_epic_imagery and get_epic_imagery_by_date could be confusing as they both retrieve EPIC images, though the latter adds date specificity. Similarly, browse_asteroids and get_asteroids_feed both provide asteroid lists but differ in browsing vs. date-based filtering.
Naming Consistency5/5
Tool names follow a highly consistent verb_noun pattern throughout, all using snake_case with 'get_' or 'browse_' prefixes followed by descriptive nouns (e.g., get_asteroid_lookup, get_earth_imagery, get_mars_rover_photos). This uniformity makes the tool set predictable and easy to navigate for an agent.
Tool Count3/5
With 21 tools, the count is borderline high for a single server, as it covers multiple domains (asteroids, Earth imagery, Mars rovers, space weather, exoplanets). While each tool is specific, the scope feels broad, potentially overwhelming for an agent. A more focused server might split these into subdomains, but the count is still manageable.
Completeness4/5
The tool set provides comprehensive coverage for accessing NASA's public APIs, including data retrieval for asteroids, Earth imagery, Mars rovers, space weather events, and exoplanets. Minor gaps exist, such as no direct update or delete operations (expected for read-only APIs) and limited filtering options in some tools, but agents can work around these with the available query parameters.
Average 3.3/5 across 21 of 21 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on April 30, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It only states the action ('browse') without detailing what 'browse' entails—e.g., whether it returns a list, supports filtering, has pagination, or involves read-only vs. mutative operations. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It is front-loaded and efficiently conveys the core purpose without redundancy or fluff, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations, no output schema, and multiple sibling tools, the description is incomplete. It doesn't explain what 'browse' returns (e.g., a list, details, or filtered data) or how it differs from other asteroid-related tools. For a tool in a complex context with no structured support, more detail is needed to guide proper usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately avoids unnecessary details. A baseline score of 4 is given since no parameters exist, and the description doesn't mislead about inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Browse the asteroid dataset' clearly states the action (browse) and resource (asteroid dataset), providing a basic purpose. However, it doesn't differentiate from sibling tools like 'get_asteroids_feed' or 'get_asteroid_lookup', leaving ambiguity about what specific browsing function this tool provides compared to alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With multiple sibling tools related to asteroids (e.g., 'get_asteroids_feed', 'get_asteroid_lookup'), the description lacks any context about use cases, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool performs a 'look up,' implying a read-only operation, but doesn't clarify aspects like data source, potential rate limits, error handling, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first and parameter details following in a structured 'Args:' section. There's no wasted text, but the parameter explanation could be slightly more detailed without sacrificing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for effective tool use. It covers the basic purpose and parameter but omits critical context like return values, error conditions, or performance characteristics. For a lookup tool with no structured metadata, this leaves users guessing about the tool's full behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal semantics beyond the input schema. It specifies that 'asteroid_id' is an 'Asteroid ID in the NASA JPL small body (SPK-ID) system,' which provides context not in the schema (which has 0% description coverage). However, with only one parameter, the baseline is high, and this addition is adequate but not comprehensive (e.g., no examples or format details).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Look up a specific asteroid based on its NASA JPL ID.' This specifies the verb ('look up'), resource ('asteroid'), and key identifier ('NASA JPL ID'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'browse_asteroids' or 'get_asteroids_feed', which might offer broader or different asteroid data access.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions the 'asteroid_id' parameter but doesn't explain scenarios where this lookup is preferred over sibling tools (e.g., 'browse_asteroids' for lists or 'get_asteroids_feed' for updates). Without such context, users must infer usage from the tool name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what the tool does without mentioning any behavioral traits such as whether it's read-only, requires authentication, has rate limits, or what the output format might be. For a tool with no annotations, this is insufficient to inform the agent about how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the main purpose in the first sentence. The additional 'Args' section is brief and relevant. There is no wasted text, making it efficient to read, though it could benefit from more structured guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not cover behavioral aspects, usage context, or detailed parameter meanings. For a tool with no structured data support, the description should provide more comprehensive information to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds some parameter semantics by listing the 'collection' options ('natural', 'enhanced'), which is not covered in the input schema (0% schema description coverage). However, it does not explain what these options mean or how they affect the results. With one parameter and low schema coverage, the description provides basic but incomplete compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get available dates for EPIC images.' It specifies the verb ('Get') and resource ('available dates for EPIC images'), making it easy to understand what the tool does. However, it does not explicitly distinguish this tool from its sibling 'get_epic_imagery' or 'get_epic_imagery_by_date', which might handle related but different functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions the 'collection' parameter options but does not explain when to choose 'natural' vs 'enhanced', nor does it reference sibling tools like 'get_epic_imagery' for context. This lack of usage context leaves the agent without clear direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action ('Get') and parameters, without mentioning permissions, rate limits, data format, or any side effects. For a data retrieval tool with no annotations, this is insufficient to inform the agent about behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the purpose and then detailing parameters in a structured 'Args:' section. It avoids unnecessary words, though it could be slightly more concise by integrating the parameter details more seamlessly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a data retrieval tool with 2 parameters), no annotations, and no output schema, the description is moderately complete. It explains the purpose and parameters well, but lacks information on return values, error handling, or data scope, which are important for contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by explaining both parameters: 'start_date' and 'end_date' with format details (YYYY-MM-DD) and default values (30 days before current date and current date). This adds significant meaning beyond the bare schema, though it does not cover all potential semantics like validation rules.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Get high speed stream (HSS) data', which clarifies the verb (get) and resource (HSS data), but it does not differentiate this from sibling tools like 'get_coronal_mass_ejection' or 'get_solar_flare', which likely retrieve similar space weather data. The purpose is clear but lacks sibling distinction, making it vague in context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description does not mention any context, exclusions, or prerequisites, such as why one would choose HSS data over other solar or space weather data from sibling tools. This leaves the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a read operation ('Get information'), implying it's non-destructive, but doesn't cover other traits like authentication needs, rate limits, error handling, or response format. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. The first sentence states the purpose clearly, followed by a parameter list with helpful details. There's no wasted text, and it's front-loaded with the core functionality. A minor deduction because the parameter explanations could be integrated more seamlessly, but overall it's efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is partially complete. It covers the purpose and parameters well but lacks usage guidelines, behavioral context, and output details. Without annotations or an output schema, the agent is left guessing about the return format and operational constraints, making this adequate but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaningful context for all parameters: 'lat' and 'lon' are for location, 'date' specifies format (YYYY-MM-DD), and 'dim' explains units (degrees) and scale (0.025 degrees ≈ 2.7 km). This goes beyond the schema's basic titles, though it doesn't cover validation rules or optionality details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get information about available imagery assets for a specific location and date.' It specifies the verb ('Get information') and resource ('imagery assets'), and distinguishes it from sibling tools like 'get_earth_imagery' by focusing on asset metadata rather than imagery itself. However, it doesn't explicitly contrast with all siblings, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_earth_imagery' or explain scenarios where asset information is needed over actual imagery. There's no context about prerequisites or exclusions, leaving the agent to infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It implies a read operation but doesn't disclose critical traits like whether it returns latest images, requires authentication, has rate limits, or provides metadata. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and well-structured, with a clear purpose statement followed by parameter details in a labeled 'Args' section. Every sentence earns its place, and there is no redundant or verbose content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns complex imagery data. It doesn't explain what the tool returns (e.g., image URLs, metadata, date ranges), how results are formatted, or any limitations, leaving the agent with insufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for the single parameter 'collection' by listing options ('natural', 'enhanced'), which compensates for the 0% schema description coverage. This clarifies the parameter's purpose beyond the schema's basic type and default, though it doesn't explain what these options entail or their differences.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get images') and resource ('from the EPIC (Earth Polychromatic Imaging Camera)'), making the purpose immediately understandable. It distinguishes from siblings like 'get_earth_imagery' by specifying the EPIC camera source. However, it doesn't explicitly contrast with 'get_epic_imagery_by_date', leaving some ambiguity about scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_epic_imagery_by_date' for date-specific queries or 'get_earth_imagery' for broader Earth imagery, nor does it specify prerequisites or contexts for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes a read operation ('Get') and implies data retrieval, but doesn't specify output format, pagination, rate limits, authentication needs, or error handling. For a tool with no annotations, this leaves significant behavioral gaps, though it's not misleading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the purpose is stated first, followed by parameter details in a clear 'Args:' section. It's efficient with no wasted sentences, though the formatting could be slightly more polished for optimal readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters with 0% schema coverage and no output schema or annotations, the description is moderately complete. It explains the parameters well but lacks details on return values, error conditions, or behavioral constraints. For a data retrieval tool in a scientific context, more context about data format or limitations would be beneficial, but it meets minimum viability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema. The schema has 0% description coverage, but the description explains both parameters: 'start_date' and 'end_date' with format details (YYYY-MM-DD) and default behaviors (30 days before current date for start_date, current date for end_date). This compensates well for the low schema coverage, though it doesn't cover all possible edge cases.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get magnetopause crossing (MPC) data.' It specifies the verb ('Get') and resource ('magnetopause crossing data'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_geomagnetic_storm' or 'get_coronal_mass_ejection', which are related space weather tools, so it doesn't reach the highest clarity level.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or explain the specific context for retrieving magnetopause crossing data compared to other space weather data tools. There's no indication of prerequisites, exclusions, or recommended scenarios for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only describes the input parameters and their defaults, but doesn't mention what the tool returns (format, structure, units), whether it's a read-only operation, potential rate limits, authentication requirements, or error conditions. For a data retrieval tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and well-structured. It opens with a clear purpose statement, then provides parameter documentation in a clean 'Args:' section. Every sentence earns its place, with no wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of space weather data retrieval, no annotations, and no output schema, the description is incomplete. While it documents parameters well, it doesn't describe what the tool returns (data format, structure, units), potential limitations, or how to interpret the RBE data. For a specialized scientific data tool, this leaves too much unspecified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial value beyond the input schema, which has 0% description coverage. It explains both parameters ('start_date' and 'end_date'), specifies their format ('YYYY-MM-DD'), and documents their default behaviors ('Defaults to 30 days before current date' and 'Defaults to current date'). This fully compensates for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get radiation belt enhancement (RBE) data.' It specifies the verb ('Get') and resource ('radiation belt enhancement data'), making it easy to understand what the tool does. However, it doesn't differentiate itself from sibling tools like 'get_coronal_mass_ejection' or 'get_solar_flare' beyond the specific data type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for when RBE data is needed, or how it differs from other space weather tools in the sibling list. The only implicit usage hint is the date parameters, but no explicit guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Get images') but lacks details on permissions, rate limits, response format, pagination, or error handling. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement followed by a bullet-point list of parameters. Every sentence adds value without redundancy, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is partially complete. It covers parameter semantics well but lacks behavioral details and usage guidelines, leaving the agent to infer aspects like response format and tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'date' is in YYYY-MM-DD format and lists 'collection' options ('natural', 'enhanced'), clarifying usage that isn't evident from the schema alone. This compensates well for the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get images') and resource ('from the EPIC'), and identifies the scope ('for a specific date'). It distinguishes from sibling tools like 'get_epic_imagery' by specifying date-based retrieval, though it doesn't explicitly contrast with all siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_epic_imagery' or other imagery tools in the sibling list. It mentions the date parameter but offers no context about prerequisites, constraints, or comparative use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data (implying a read-only operation) and mentions date parameters with defaults, but lacks critical details: it doesn't specify data format, source, rate limits, authentication needs, error conditions, or whether it's a real-time or historical query. For a data retrieval tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and well-structured: a clear purpose statement followed by a parameter list with concise explanations. Every sentence adds value—none are redundant or vague. It could be slightly more front-loaded by integrating parameter hints into the main sentence, but overall it's efficient and readable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of solar data retrieval, no annotations, and no output schema, the description is incomplete. It covers parameters well but omits essential context: output format (e.g., JSON, CSV), data granularity, source reliability, error handling, and how it differs from sibling solar tools. Without this, an agent might struggle to use the tool effectively or interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant value beyond the input schema, which has 0% description coverage. It explicitly defines both parameters ('start_date' and 'end_date'), specifies their format ('YYYY-MM-DD'), and provides default behaviors ('Defaults to 30 days before current date' and 'Defaults to current date'). This fully compensates for the schema's lack of descriptions, making parameter usage clear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get solar energetic particle (SEP) data.' This specifies the verb ('Get') and resource ('solar energetic particle data'), making the function unambiguous. However, it doesn't differentiate from sibling tools like 'get_coronal_mass_ejection' or 'get_solar_flare' that also retrieve solar event data, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_coronal_mass_ejection' or 'get_solar_flare' for comparison, nor does it specify use cases, prerequisites, or exclusions. The only implicit context is the date parameters, but this doesn't constitute meaningful usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the 7-day limit for the feed, which is a useful behavioral constraint. However, it lacks details on other traits such as rate limits, authentication needs, error handling, or the format of the returned list (e.g., pagination, data fields). For a tool with no annotations, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by a structured 'Args:' section that explains parameters efficiently. There's no wasted text, and the information is organized for quick comprehension, though it could be slightly more polished in formatting.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 0% schema description coverage, the description provides basic context like purpose and parameter semantics. However, it's incomplete for a tool that likely returns a list of asteroids: it doesn't describe the output format, potential errors, or other behavioral aspects. For a data retrieval tool, this leaves the agent with insufficient information to fully understand what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics: 'start_date' and 'end_date' are defined as dates for asteroid search in YYYY-MM-DD format, and it explains that the 'end_date' defaults to 7 days after 'start_date' if not specified, with a 7-day limit. This clarifies usage beyond the bare schema, though it doesn't cover all potential edge cases or validation rules.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get a list of asteroids based on their closest approach date to Earth.' It specifies the verb ('Get'), resource ('list of asteroids'), and key criterion ('closest approach date to Earth'). However, it doesn't explicitly differentiate from siblings like 'browse_asteroids' or 'get_asteroid_lookup,' which might offer different asteroid-related functionalities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning the date-based search and a 7-day limit, suggesting it's for retrieving asteroids approaching Earth within a specific timeframe. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'browse_asteroids' or 'get_asteroid_lookup,' nor does it specify exclusions or prerequisites beyond the date constraints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that the tool 'gets' data, implying a read-only operation, but doesn't mention rate limits, authentication needs, data format, pagination, or error handling. For a tool with no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the purpose stated first followed by parameter details. It uses bullet points for clarity and avoids unnecessary information. However, the 'Args:' section could be slightly more integrated into the flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is partially complete. It covers parameters well but lacks context about the data returned, error cases, or how it fits with sibling tools. Without annotations or output schema, more behavioral details would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial value beyond the input schema, which has 0% description coverage. It explains both parameters ('start_date' and 'end_date'), specifies their format ('YYYY-MM-DD'), and provides default values ('30 days before current date' and 'current date'). This fully compensates for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get coronal mass ejection (CME) data.' It specifies the verb ('Get') and resource ('coronal mass ejection data'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_solar_flare' or 'get_geomagnetic_storm', which are related solar/space weather tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools, context for CME data retrieval, or any prerequisites. The only implicit usage hint is the date parameters, but this doesn't help the agent choose between similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that 'cloud_score' is 'currently not available,' which is useful context about a limitation. However, it doesn't describe what the tool returns (e.g., image format, size, metadata), error conditions, rate limits, or authentication requirements. For a tool with no annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement followed by parameter explanations in a labeled 'Args:' section. Each parameter explanation is concise and informative. The only minor inefficiency is repeating 'Image' in the date parameter description, but overall it's appropriately sized and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, no annotations, no output schema), the description is moderately complete. It excels at parameter semantics but lacks information about return values, error handling, and usage context relative to siblings. Without an output schema, the description should ideally explain what the tool returns, but it doesn't. This leaves the agent uncertain about the output format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides excellent parameter semantics beyond the input schema, which has 0% description coverage. It explains what each parameter means: 'lat' and 'lon' as coordinates, 'date' format and default behavior, 'dim' as width/height in degrees with a real-world conversion (2.7 km), and 'cloud_score' functionality and current limitation. This fully compensates for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get Earth imagery from Landsat 8 satellite.' It specifies the resource (Earth imagery) and source (Landsat 8 satellite). However, it doesn't explicitly differentiate from sibling tools like 'get_earth_assets' or 'get_epic_imagery', which might also provide Earth imagery from different sources or with different characteristics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'get_earth_assets' and 'get_epic_imagery' available, there's no indication of what makes this tool unique or when it should be preferred. The only usage context is implicit through parameter descriptions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes what the tool does (fetching data) and lists parameters, but lacks behavioral details such as rate limits, authentication needs, error handling, or response structure. For a data-fetching tool with external API dependencies, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It starts with a clear purpose statement, followed by a bullet-point-like 'Args' section that efficiently details each parameter with examples and options. Every sentence adds value, though it could be slightly more front-loaded with a brief usage hint.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is moderately complete. It covers parameters well but lacks behavioral context (e.g., network dependencies, error cases) and output details. For a tool fetching data from an external archive with multiple parameters, this leaves gaps in understanding how to interpret results or handle failures.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It adds substantial meaning beyond the schema: it explains each parameter's purpose ('query' for filtering with syntax examples, 'table' with common options and explanations, 'format' with options and default), providing clear semantics that the schema lacks. This effectively documents all three parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get data from NASA's Exoplanet Archive.' It specifies the verb ('Get') and resource ('data from NASA's Exoplanet Archive'), making the action clear. However, it doesn't explicitly differentiate this tool from its siblings (which focus on asteroids, Earth, Mars, solar events, etc.), though the domain difference is implicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions common table options but doesn't explain scenarios for choosing 'exoplanets' over 'cumulative' or other tables, nor does it reference sibling tools or broader context for data selection. Usage is implied through parameter details but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states 'Get DONKI notifications' which implies a read-only operation, but doesn't disclose behavioral traits like rate limits, authentication needs, pagination, error handling, or what 'DONKI' stands for (NASA's Space Weather Database). For a tool with no annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with a clear purpose statement followed by parameter details. The 'Args:' section is well-structured. It could be slightly more concise by integrating defaults into the purpose statement, but overall it's efficient with minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description does well on parameters but lacks behavioral context and output details. For a 3-parameter tool with 0% schema coverage, it's minimally adequate but leaves gaps in understanding the tool's full behavior and results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It fully documents all 3 parameters: start_date (format YYYY-MM-DD, default 7 days before current), end_date (same format, default current date), and notification_type (options: all, FLR, SEP, CME, IPS, MPC, GST, RBE, report with default 'all'). This adds complete meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'DONKI notifications', making the purpose specific and understandable. It distinguishes this tool from sibling tools that focus on asteroids, Earth imagery, Mars rovers, and other space-related data. However, it doesn't explicitly differentiate from other notification-related tools (none exist in siblings), so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for selecting notification types, or comparison with sibling tools. The agent must infer usage from the tool name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action ('Get') and parameters, without mentioning any behavioral traits like rate limits, authentication needs, data format, or potential side effects. For a data retrieval tool, this leaves significant gaps in understanding how it operates beyond basic parameter usage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose followed by parameter details. It uses bullet points for parameters, making it easy to scan. However, the 'Args:' section could be slightly more integrated, and there's some minor redundancy in stating 'Defaults to' for both dates, but overall it's efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers parameters well but lacks behavioral context and usage guidelines. For a simple data retrieval tool, this is minimally adequate, but it doesn't fully address what the tool returns or how it interacts with the system, leaving room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains both parameters (start_date and end_date) with details on format (YYYY-MM-DD) and default values (30 days before current date, current date). This fully compensates for the schema's lack of documentation, providing clear and necessary context for parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get solar flare (FLR) data.' It specifies the resource (solar flare data) and the action (get/retrieve), which is straightforward. However, it doesn't explicitly differentiate from sibling tools like 'get_coronal_mass_ejection' or 'get_solar_energetic_particle', which might also retrieve solar-related data, leaving some ambiguity about its specific scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where other tools might be more appropriate, such as for different solar events or data types. This lack of comparative information limits the agent's ability to choose correctly among related options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that it 'gets' data, implying a read-only operation, but doesn't cover critical aspects like authentication requirements, rate limits, data format, error handling, or whether it returns real-time or historical data. This leaves significant gaps for an agent to understand how to interact with it effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose in the first sentence, followed by parameter details in a clear 'Args:' section. Every sentence earns its place by providing essential information without redundancy, though it could be slightly more structured with bullet points.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and parameters well but lacks context about the data's nature, usage scenarios, or behavioral traits. Without annotations or output schema, more detail on what the tool returns would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial value beyond the input schema, which has 0% description coverage. It explicitly defines both parameters (start_date and end_date), specifies their format (YYYY-MM-DD), and provides default behaviors (7 days before current date for start_date, current date for end_date). This fully compensates for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Get WSA+Enlil simulation data' with a specific verb ('Get') and resource ('WSA+Enlil simulation data'), distinguishing it from sibling tools focused on asteroids, Earth imagery, Mars rovers, and other space phenomena. However, it doesn't explicitly differentiate from potential similar data-fetching tools beyond naming the specific dataset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description lacks context about what WSA+Enlil simulation data is used for, prerequisites, or comparisons with sibling tools like get_coronal_mass_ejection or get_solar_flare that might relate to solar activity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes a read operation ('Get') and specifies date parameters with defaults, but doesn't cover critical aspects like data format, rate limits, authentication needs, error handling, or whether the tool is idempotent. For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded. The first sentence states the core purpose, followed by a structured 'Args:' section that efficiently documents parameters without redundancy. Every sentence earns its place, and there's no wasted verbiage or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is partially complete. It excels in parameter documentation but lacks context on output format, data scope, or integration with sibling tools. Without annotations or output schema, the agent won't know what the returned data looks like or how to handle it, leaving gaps in overall usability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explicitly documents both parameters ('start_date' and 'end_date'), their formats ('YYYY-MM-DD'), and default behaviors ('Defaults to 30 days before current date' and 'Defaults to current date'). This fully compensates for the schema's lack of descriptions, providing clear and complete parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get geomagnetic storm (GST) data.' It specifies the verb ('Get') and resource ('geomagnetic storm data'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_coronal_mass_ejection' or 'get_solar_flare', which likely retrieve related but distinct space weather data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where geomagnetic storm data is preferred over other space weather data (e.g., solar flares or coronal mass ejections). The only implied usage is retrieving GST data within a date range, but no exclusions or prerequisites are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the data returned (mission details) but lacks behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, or error handling. The description does not contradict annotations, but it fails to disclose key operational aspects beyond the basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose followed by details and parameter explanation. Every sentence adds value, though the structure could be slightly more streamlined by integrating the parameter details more seamlessly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (1 parameter, no output schema, no annotations), the description covers the basic purpose and parameter semantics adequately. However, it lacks details on behavioral aspects (e.g., read-only nature, error cases) and does not fully compensate for the absence of annotations and output schema, leaving gaps in operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and 1 parameter, the description compensates by explaining the parameter's purpose ('Name of the rover') and providing valid values (curiosity, opportunity, spirit). This adds meaningful context beyond the bare schema, though it could specify format constraints (e.g., lowercase).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get the mission manifest') and resource ('Mars rover'), listing the exact rovers (Curiosity, Opportunity, Spirit) and detailing the mission information provided (landing/launch dates, status, etc.). This distinguishes it from sibling tools like get_mars_rover_photos, which focuses on photos rather than manifest data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying the rover names and the type of data returned, but it does not explicitly state when to use this tool versus alternatives (e.g., get_mars_rover_photos for photos instead of manifest details). No exclusions or prerequisites are mentioned, leaving some ambiguity for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses behavioral traits: it retrieves data (implied read-only), handles defaults (current date if 'date' not specified), and describes parameter interactions (e.g., 'count' cannot be used with 'date'). However, it lacks details on rate limits, authentication needs, or error handling, which are important for a public API tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose followed by detailed Args. Every sentence earns its place by clarifying parameter usage. However, it could be slightly more concise by integrating the purpose and Args more seamlessly, and the structure is functional but not optimal for quick scanning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (3 parameters, no annotations, no output schema), the description is moderately complete. It explains parameters well and provides usage guidelines, but lacks output details (e.g., what data is returned) and broader context like API limitations or error cases. For a tool with no structured output, more information on return values would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate fully. It adds significant meaning beyond the input schema by explaining each parameter's purpose, format (e.g., 'YYYY-MM-DD'), defaults, constraints (e.g., 'count' vs. 'date' exclusion), and behavior (e.g., 'thumbs' ignored for non-videos). This comprehensively covers all three parameters, providing essential context not in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get NASA's astronomy picture of the day.' It specifies the verb 'Get' and the resource 'NASA's astronomy picture of the day,' which is distinct from sibling tools like those for asteroids, Mars rovers, or solar events. However, it does not explicitly differentiate from all siblings, as some might also retrieve images (e.g., get_earth_imagery), so it lacks full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage through the Args section, explaining when to use parameters like 'date' for specific dates, 'count' for random images, and 'thumbs' for videos. It includes an exclusion rule: 'Cannot be used with 'date'' for 'count.' However, it does not explicitly state when to use this tool versus alternatives among siblings, such as for astronomy-specific images versus other NASA imagery tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses basic behavioral traits like the exclusive choice between sol and earth_date, and pagination (25 photos per page), but lacks details on rate limits, error handling, or authentication needs. This is adequate but has gaps for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by a structured list of parameters with clear explanations. Every sentence adds value without redundancy, making it efficient and easy for an AI agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, no annotations, no output schema), the description is largely complete, covering purpose, usage rules, and parameter details. However, it lacks information on return values or error cases, which would enhance completeness for a tool with no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates well by explaining each parameter's purpose and constraints (e.g., sol vs. earth_date exclusivity, camera abbreviations, page usage). It adds meaningful semantics beyond the bare schema, though it could provide more on default behaviors or validation rules.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get photos') and resource ('from a Mars rover'), and it distinguishes the tool from siblings by specifying the rovers (Curiosity, Opportunity, Spirit). This is precise and actionable for an AI agent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance by stating 'Specify either sol (Martian day) or earth_date (YYYY-MM-DD), but not both,' which helps the agent avoid conflicts. It also lists camera options and pagination details, offering clear context for when to use parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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